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Automatic detection of reverse-triggering related asynchronies during mechanical ventilation in ARDS patients using flow and pressure signals

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Abstract

Asynchrony due to reverse-triggering (RT) may appear in ARDS patients. The objective of this study is to validate an algorithm developed to detect these alterations in patient–ventilator interaction. We developed an algorithm that uses flow and airway pressure signals to classify breaths as normal, RT with or without breath stacking (BS) and patient initiated double-triggering (DT). The diagnostic performance of the algorithm was validated using two datasets of breaths, that are classified as stated above. The first dataset classification was based on visual inspection of esophageal pressure (Pes) signal from 699 breaths recorded from 11 ARDS patients. The other classification was obtained by vote of a group of 7 experts (2 physicians and 5 respiratory therapists, who were trained in ICU), who evaluated 1881 breaths gathered from recordings from 99 subjects. Experts used airway pressure and flow signals for breaths classification. The RT with or without BS represented 19% and 37% of breaths in Pes dataset while their frequency in the expert’s dataset were 3% and 12%, respectively. The DT was very infrequent in both datasets. Algorithm classification accuracy was 0.92 (95% CI 0.89–0.94, P < 0.001) and 0.96 (95% CI 0.95–0.97, P < 0.001), in comparison with Pes and experts’ opinion. Kappa statistics were 0.86 and 0.84, respectively. The algorithm precision, sensitivity and specificity for individual asynchronies were excellent. The algorithm yields an excellent accuracy for detecting clinically relevant asynchronies related to RT.

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Acknowledgements

We would like to thank Prof. Laurent Brochard for his help in interpreting reverse-triggering asynchronies and their classification.

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Authors and Affiliations

  1. Centro de Educación Médica e Investigaciones Clínicas “Norberto Quirno” (CEMIC), Av. Cnel. Díaz 2423, Buenos Aires, 1425, Argentina

    Pablo O. Rodriguez, Ignacio Bonelli, Luis P. Maskin & Mariano Setten

  2. Instituto Universitario CEMIC (IUC), Buenos Aires, Argentina

    Pablo O. Rodriguez, Ignacio Bonelli, Luis P. Maskin & Mariano Setten

  3. Complejo Médico de la Policía Federal Argentina Churruca Visca, Buenos Aires, Argentina

    Norberto Tiribelli, Sebastian Fredes & María Guaymas

  4. Hospital Italiano de Buenos Aires (HIBA), Buenos Aires, Argentina

    Emiliano Gogniat, Ignacio Fernandez Ceballos & Eduardo San Roman

  5. Sanatorio Anchorena Recoleta, Buenos Aires, Argentina

    Gustavo A. Plotnikow & Romina A. Pratto

  6. Sanatorio de la Trinidad Mitre, Buenos Aires, Argentina

    Sebastian Fredes & Santiago Ilutovich

  7. Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires, Argentina

    Matias Madorno

  8. MBMED S.A., Buenos Aires, Argentina

    Matias Madorno

  9. Emeritus Professor of Internal Medicine, Facultad de Medicina, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina

    Alejandro C. Raimondi

  10. Facultad de Medicina, Universidad del Salvador, Buenos Aires, Argentina

    Mariano Setten

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  1. Pablo O. Rodriguez
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  2. Norberto Tiribelli
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  3. Emiliano Gogniat
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  4. Gustavo A. Plotnikow
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  8. Matias Madorno
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  9. Santiago Ilutovich
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  10. Eduardo San Roman
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  11. Ignacio Bonelli
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  13. Alejandro C. Raimondi
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  14. Luis P. Maskin
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  15. Mariano Setten
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Consortia

GRAAVEplus (Grupo Argentino de estudio de Asincronías en la VEntilación mecanica y temas relacionados a los cuidados críticos)

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Correspondence to Pablo O. Rodriguez.

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Matías Madorno is a partner and manager of MBMED S.A. The other authors declare that there is no conflict of interest.

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Rodriguez, P.O., Tiribelli, N., Gogniat, E. et al. Automatic detection of reverse-triggering related asynchronies during mechanical ventilation in ARDS patients using flow and pressure signals. J Clin Monit Comput 34, 1239–1246 (2020). https://doi.org/10.1007/s10877-019-00444-3

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  • DOI: https://doi.org/10.1007/s10877-019-00444-3

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